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. Author manuscript; available in PMC: 2017 Nov 1.
Published in final edited form as: Drug Alcohol Depend. 2016 Sep 19;168:335–339. doi: 10.1016/j.drugalcdep.2016.08.009

The Hazards of Bad Sleep – Sleep Duration and Quality as Predictors of Adolescent Alcohol and Cannabis Use*

Thomas B Mike a, Daniel S Shaw b, Erika E Forbes a, Stephanie L Sitnick c, Brant P Hasler a,#
PMCID: PMC5086262  NIHMSID: NIHMS815486  PMID: 27659736

Abstract

Background

Although an association between adolescent sleep and substance use is supported by the literature, few studies have characterized the longitudinal relationship between early adolescent sleep and subsequent substance use. The current study examined the prospective association between the duration and quality of sleep at age 11 and alcohol and cannabis use throughout adolescence.

Methods

The present study, drawn from a cohort of 310 boys taking part in a longitudinal study in Western Pennsylvania, includes 186 boys whose mothers completed the Child Sleep Questionnaire; sleep duration and quality at age 11 were calculated based on these reports. At ages 20 and 22, participants were interviewed regarding lifetime alcohol and cannabis use. Cox proportional hazard analysis was used to determine the association between sleep and substance use.

Results

After accounting for race, socioeconomic status, neighborhood danger, active distraction, internalizing problems, and externalizing problems, both the duration and quality of sleep at age 11 were associated with multiple earlier substance use outcomes. Specifically, less sleep was associated with earlier use, intoxication, and repeated use of both alcohol and cannabis. Lower sleep quality was associated with earlier alcohol use, intoxication, and repeated use. Additionally, lower sleep quality was associated with earlier cannabis intoxication and repeated use, but not first use.

Conclusions

Both sleep duration and sleep quality in early adolescence may have implications for the development of alcohol and cannabis use throughout adolescence. Further studies to understand the mechanisms linking sleep and substance use are warranted.

Keywords: Alcohol, cannabis, sleep, adolescence

1. INTRODUCTION

Despite efforts to curb adolescent substance use, alcohol use remains high and cannabis use has been increasing (Johnston et al., 2014), suggesting work is needed to understand the risk factors underlying substance use during this unique developmental period. Many risk factors are well described, including internalizing and externalizing problems (Bongers et al., 2003), self-regulation (Wills et al., 1995), and reward regulation (Steinberg, 2007). Sleep, associated with all of these factors (Gregory and O’Connor, 2002; Hasler et al., 2012a; Killgore et al., 2006; Pesonen et al., 2010), changes dramatically in adolescence, with restriction, insomnia, and delayed timing being particularly prominent (Carskadon et al., 2004; Johnson et al., 2006). Yet despite evidence linking multiple sleep constructs to adolescent substance use (Bootzin and Stevens, 2005; Hasler et al., 2012b; McKnight-Eily et al., 2011; Wong et al., 2015), adolescent sleep disturbance remains under-explored.

Sleep disturbance repeatedly has been linked to adolescent substance use. Cross-sectional studies associate sleep problems with alcohol use (Pieters et al., 2010). Sleep duration (McKnight-Eily et al., 2011), self-reported sleep problems (Johnson and Breslau, 2001), and insomnia symptoms (Roane and Taylor, 2008) all have been related to concurrent alcohol and cannabis use. Prospective evidence suggests that sleep disturbance precedes substance use. Parent reports of childhood “trouble sleeping” and “overtiredness” predicted boys’ alcohol and cannabis use between ages 12 and 14 (Wong et al., 2004), while adolescent self-endorsement of these behaviors predicted subsequent illicit drug use (Wong et al., 2010). Insomnia in 12 to 19 year-olds was associated with alcohol use one year later (Hasler et al., 2014). In the National Longitudinal Study of Adolescent Health, trouble falling asleep and decreased sleep duration were associated with subsequent illicit drug use (Wong et al., 2015). Finally, sleep duration predicted marijuana use two years later (Pasch et al., 2012). This evidence suggests that pediatric sleep is associated with alcohol and cannabis use, but long-term longitudinal studies of adolescent sleep and substance remain rare.

To lend further credence to sleep as a risk factor for adolescent substance use, additional evidence of a temporal relationship between the two is required. In the present project, we hypothesized longitudinal associations between difficulties in early adolescent sleep and substance use among urban, low-income boys. We expected shorter sleep duration and worse sleep quality at age 11 would be associated with earlier and greater alcohol and cannabis use throughout adolescence after accounting for potential third variables. We focused on early adolescent sleep because it captures a key point when puberty-related sleep changes first occur (Kim et al., 2002; Knutson, 2005; Laberge et al., 2001). Additionally, we thought it would be informative to assess sleep before age 14 because initiating substance use by this age increases one’s risk of developing a substance use disorder (DeWit et al., 2000; Hingson et al., 2006). By examining substance use from age 12 through 22, we hope to more precisely describe the relationship between adolescent sleep and subsequent substance use.

2. MATERIALS AND METHODS

2.1 Participants

Data utilized came from the Pitt Mother & Child Project, a longitudinal study on vulnerability, resilience, and antisocial behavior in low-socioeconomic status (SES) boys (Shaw et al., 2003, 1999). Unadjusted analyses include 173 and 165 boys for alcohol and cannabis, respectively, drawn from the originally recruited sample of 310 boys and the subset of 186 who completed the sleep assessment (added partway through the age 11 assessment1). Of those 186, some were excluded from the respective analyses for missing data on alcohol (n=13) or cannabis (n=21). Adjusted analyses with all covariates include 145 and 140 boys for alcohol and cannabis, respectively. Other participants were excluded from adjusted analyses for missing data on neighborhood danger (n=1), internalizing/externalizing problem behavior (n=1), and active distraction (n=30). Mann-Whitney U tests show included and excluded participants had similar demographic characteristics at study onset.

2.2 Variables

2.2.1 Sleep Measures

When participants were 11, mothers completed the Child Sleep Questionnaire2 (CSQ), an adaptation of the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989). Sleep duration was calculated by subtracting night-time wakefulness (time to fall asleep, waking mid-sleep, and waking up early) from estimated time in bed. The scoring system for sleep quality, designed to closely mimic the PSQI, summed 7 variables on a 0–3 scale (continuous variables were recoded as Likert items) with higher numbers indicating worse sleep (Cronbach α=0.63). Variables included sleep duration, sleep latency, mid-sleep disturbance, late-sleep disturbance, maternal impression of sleep quality, day disturbance, and sleep efficiency.

2.2.2 Substance Use

At age 20, participants were interviewed using the Lifetime Drug and Alcohol Use History (Clark et al., 2001; Skinner and Sheu, 1982). Initial and later use of alcohol and cannabis was coded from this interview and again at age 22 to examine use since age 20. Of note, 22 individuals were interviewed only at age 22 due to availability; these participants provided information on their substance use similar to those interviewed at age 20. Variables included age of first use, intoxication, and repeated use, defined as the age when the participant used alcohol ten times or cannabis three times in a year.

2.2.3 Covariates

Minority status (coded dichotomously as White or non-White) was included because of sleep and substance use differences between Whites and Blacks (Chen and Jacobson, 2012; Chen et al., 2015). Family SES at age 11 was measured using the Hollingshead four-factor index score which accounts for parental marital status, education, occupation, and sex (Hollingshead, 1975).

Internalizing and externalizing problems were accounted for given their links to substance use (King et al., 2004; O’Neil et al., 2011). When participants were 11, mothers completed the Child Behavior Checklist, a well-established assessment of child behavior (Achenbach, 1991). The broad-band internalizing (Cronbach α=0.86) and externalizing (Cronbach α=0.92) problem scores were used as covariates.

Neighborhood danger was accounted for because of greater exposure to substances and traumatic events for youth in high-risk neighborhoods (Lowry et al., 1999; Zinzow et al., 2009). At age 11, mothers filled out the Me and My Neighborhood questionnaire which includes 18 Likert-scale items relating to neighborhood danger (e.g., “a friend was stabbed or shot”); higher summed scores indicate increased danger (α=0.88) (PMCP, 2001).

Emotion regulation was accounted for because of its association with substance use (Wills et al., 1995). At age 3.5, child behavior was coded from videotapes of a 3-minute cookie delay-of-gratification task, in which the child has nothing to do while mother fills out a questionnaire and holds onto a clear bag containing a preferred cookie (Marvin, 1977). The number of 10-second intervals spent in active distraction, directly inversely linked to later child problem behavior (Gilliom et al., 2002), was used as a covariate (mean=10.92, SD=5.14).

2.3 Data Analysis

Cox regression was used to model the association between sleep and substance use. The assumption of proportional hazards was tested using Schoenfeld residuals in R 3.2.3. These data do not violate the assumption of proportional hazards.

Hazard models were created using SPSS 23’s “Cox Regression” function with an α=0.05 (Table 1). Individuals were left-censored if they used the substance of interest before age 12 (n=10 for alcohol; n=4 for cannabis). Unadjusted analyses included the sleep variables as continuous variables. Adjusted analyses added all covariates. To facilitate interpretation, the reciprocal sleep duration hazard ratios were recorded.

Table 1.

Age 11 sleep duration and quality as predictors of alcohol and cannabis use in cox regression models

Predictor
Sleep Duration
Sleep Quality
Outcome HR(95% CI) p HR(95% CI) p
Unadjusted Analysis
 Alcohol
  First Use 1.18 (1.04–1.34) .01 1.05 (1.00–1.11) .08
  Intoxication 1.20 (1.04–1.38) .01 1.06 (1.00–1.12) .05
  Repeated Use 1.15 (1.00–1.32) .06 1.05 (0.99–1.11) .10
 Cannabis
  First Use 1.24 (1.06–1.45) .006 1.09 (1.02–1.16) .007
  Intoxication 1.24 (1.06–1.45) .008 1.09 (1.02–1.15) .01
  Repeated Use 1.27 (1.08–1.49) .004 1.09 (1.03–1.16) .005
Adjusted Analysis
 Alcohol
  First Use 1.23 (1.04–1.45) .01 1.09 (1.02–1.18) .02
  Intoxication 1.27 (1.06–1.52) .01 1.10 (1.02–1.19) .01
  Repeated Use 1.28 (1.06–1.54) .01 1.12 (1.03–121) .005
 Cannabis
  First Use 1.20 (1.01–1.41) .04 1.08 (1.00–1.16) .29
  Intoxication 1.23 (1.04–1.46) .02 1.10 (1.03–1.19) .01
  Repeated Use 1.29 (1.08–1.55) .004 1.13 (1.05–1.22) .001
Open in a new tab

Note. HR = Hazard Ration, CI = Confidence Interval. Adjusted analysis includes the following covariates in the model: race, socioeconomic status, neighborhood danger, externalizing problem behavior, internalizing problem behavior, and active distraction. Sleep duration HRs have been transformed by taking the reciprocal. HRs >1 indicate here that each 1 hour of less sleep duration was associated with x% earlier substance use.

To graphically present results, participants were grouped based on duration (approximate tertiles) and quality (scores of 0–1 in the first group, scores of 2 in the second, and scores of 3+ the third) and one-minus-survival curves for first alcohol and cannabis use were created (Figures 1a–1d).

Figure 1.

Figure 1

Open in a new tab

For the sake of interpretation, groups were created for both duration and quality and used in regression with all covariates. Earlier alcohol use was noted in groups with lower sleep duration (1a) and worse sleep quality (1b). Likewise, earlier cannabis use was noted in groups with lower sleep duration (1c) and worse sleep quality (1d).

3. RESULTS

For the unadjusted alcohol analyses, less sleep duration was associated with earlier use and intoxication, but not repeated use. Sleep quality was not associated with any outcome. Adjusted analyses showed less sleep duration and lower quality were both associated with earlier alcohol use, intoxication, and repeated use. Greater active distraction was positively associated with earlier alcohol use (HR1.04, p=0.03) and repeated use (HR1.04, p=0.03) in the sleep duration analysis. Covariates were otherwise non-significant.

For the unadjusted cannabis analyses, less sleep duration and lower sleep quality were associated with earlier use, intoxication, and repeated use. Adjusted analyses showed shorter sleep duration was associated with all earlier outcomes. Lower sleep quality was associated with earlier cannabis intoxication and repeated use, but not first use. Higher externalizing problems were associated with earlier cannabis use (HR1.04, p=0.02) and intoxication (HR1.04, p=0.04) in the duration analyses and earlier cannabis use (HR1.04, p=0.046) in the quality analysis. Covariates were otherwise non-significant.

Of note, both quality and duration were non-significant when simultaneously used in regression for all analyses.

Illustrations of group-level hazard analyses (Figure 1a–1d) imply that less sleep duration and worse sleep quality were associated with earlier substance use.

4. DISCUSSION

These results suggest that shorter duration and lower quality of sleep are risk factors for alcohol and cannabis use even when accounting for important covariates. These sleep constructs may pre-dispose adolescents to substance use, a worrying finding considering early substance use is associated with a shortened time to dependence (Clark et al., 1998).

These findings are convergent with prospective studies of sleep’s predictive role in adolescent alcohol and cannabis use (Hasler et al., 2014; Pasch et al., 2012; Wong et al., 2004, 2009, 2015) and a recent CDC report where lower sleep duration was associated with high school students’ risky behavior (Wheaton, 2016). This study is unique in linking two sleep constructs in early adolescence to alcohol and cannabis initiation, intoxication, and repeated use. As there is a dearth of literature linking early adolescent sleep to subsequent substance use, this study provides initial evidence for sleep’s developmental role in substance use.

Sleep disturbance may be an important target of interventions to decrease substance use. For pediatricians, these results warrant the validation of sleep-related screening tools to help identify at-risk adolescents. While effective behavioral treatments for sleep disturbance are available (Mindell and Meltzer, 2008), the area remains under-explored; these results highlight the need to further develop treatment strategies. These results could also suggest policy-level means to decrease substance use, especially as delaying school start times has been shown to improve sleep duration (Owens et al., 2010).

The study includes notable limitations. Importantly, the CSQ’s internal consistency was low, and sleep quality and duration were moderately correlated. Additional research disentangling these constructs – perhaps using actigraphy and sleep diaries – would be informative. Participants resided in Pennsylvania where recreational cannabis use is illegal; thus, these results may not be generalizable to other locations. This sample consisted solely of low-income, urban boys, limiting generalizability to other boys and young women, especially given evidence that sleep does not predict girls’ alcohol and cannabis use (Wong et al., 2009). Although the present study controlled for multiple covariates, additional risk factors to substance use, including parental use, peer use, and access to substances, could not be included. Finally, much of our data is retrospective and subject to memory distortions.

While these results support sleep’s role in adolescent substance use, the extant literature indicates that future work should consider how physical and psychiatric illnesses relate to sleep and substance use. Prior longitudinal data links early childhood sleep problems to adolescent anxiety and depression (Gregory and O’Connor, 2002) and supports bidirectional associations between sleep problems and generalized anxiety disorder and depression in late childhood through adolescence (Shanahan et al., 2014). Less sleep during adolescence is linked to depressive symptoms, alcohol use (Pasch et al., 2010), and comorbid physical conditions (McKnight-Eily et al., 2011). Relatedly, active distraction and externalizing problems were associated with some substance use outcomes in the present study, suggesting future longitudinal studies should consider these variables as potential moderators between sleep and substance use.

In conclusion, these results indicate that sleep is an important risk factor for adolescent substance use. The dramatic normative changes in sleep and emerging substance use during adolescence indicate that this issue deserves strong attention. While more work is needed to understand the role of physical and mental illness, these findings suggest sleep may be a target to decrease substance use throughout adolescence.

Supplementary Material

supplement

Highlights.

  • Childhood sleep problems may be prospectively linked to adolescent substance use

  • Less sleep predicted earlier onset of alcohol and cannabis involvement

  • Worse sleep quality predicted earlier onset of alcohol and cannabis involvement

  • These associations generally held after accounting for various covariates

  • Childhood sleep is a promising target for reducing adolescent substance use risk

Acknowledgments

Role of Funding Source

This research was funded by several grants through the National Institutes of Health. The National Institutes of Health provided financial support for the project and the preparation of the manuscript but did not have a role in the design of the study, the analysis of the data, the writing of the manuscript, nor the decision to submit the present research.

The authors would like to thank the participants and their parents who participated in this study. This work was supported by grants from the National Institutes of Health, including MH050907 (Shaw), DA026222 (Forbes, Shaw), T32HL082610 (Buysse) and K01DA032557 (Hasler).

Footnotes

*

Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…

1

Please see Supplementary Table 1 by accessing the online version of this paper at http://dx.doi.org and by entering doi:…

2

Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi:…

Conflicts of Interest

None.

Contributors

The Pitt Mother and Child Project was originally conceived by Daniel Shaw. The present project was designed by Thomas Mike, Erika Forbes, and Brant Hasler. Data analysis was performed by Thomas Mike, Stephanie Sitnick, and Brant Hasler. The manuscript was prepared primarily by Thomas Mike and Brant Hasler with a number of edits from Daniel Shaw, Erika Forbes, and Stephanie Sitnick. All authors were in agreement with the final submitted manuscript.

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